Predicting Mortality of ICU Patients: The PhysioNet/Computing in Cardiology Challenge 2012 1.0.0

function [prob,died]=physionet2012_D(tm,category,val)
% [prob,died]=physionet2012(tm,category,val) 
%   Submission for the PhysioNet 2012 Challenge.
%
%	Inputs:
% tm      - (Nx1 Cell Array) Cell array containing time of measurement
% category- (Nx1 Cell Array) Cell array containing type (category)
%           measurement
% value   - (Nx1 Cell Array) Cell array containing value of measurement
%
%	Outputs:
% prob    - (Scalar) Probability value of the patient dying in the hospital
% died    - (Logical) Binary classification if the patient is going to die
%           on the hospital (1 - Died, 0 - Survived)
%
%	Example
%       [prob,died]=physionet2012(tm,category,val)

%	Copyright 2012 Alistair Johnson

%	$LastChangedBy: alistair $
%	$LastChangedDate: 2012-04-25 01:27:43 +0100 (Wed, 25 Apr 2012) $
%	$Revision: 345 $
%	Originally written on GLNXA64 by Alistair Johnson, 24-Apr-2012 14:02:25
%	Contact: alistairewj@gmail.com

%=== Miscellanious default values
T=0.338; % Mortality threshold that maximizes SE/PPV

%=======================%
%=== PREPROCESS DATA ===%
%=======================%
%=== Put into expected format for function
% Convert time from string to numeric minutes
if nargin<3
    if iscell(tm) && size(tm,2)>=3
        data = tm; % assume multi-observation input
    else
        error('Incorrect input.');
    end
elseif nargin==3
    tm = cellfun(@(x) str2double(x(1:2)), tm)*60 + cellfun(@(x) str2double(x(4:5)),tm);
    data = [{1},{tm},{category},{val}];
end
[ data ] = pnPreprocess(data);

%=====================%
%=== LOAD FEATURES ===%
%=====================%
[ X ] = pniExtractFeaturesD(data);

%===================%
%=== CLASSIFY ======%
%===================%
[ prob ] = pniClassifyD(X);

%===================%
%=== SET OUTPUTS ===%
%===================%
died = prob >= T; % Thresholded probability to maximize PPV/Sens

end